The growing share of renewable energy generation based on fluctuating wind and solar energy sources is increasingly challenging in terms of power grid stability. Industrial demand-side response presents a promising way to balance energy supply and consumption. For this, energy demand is flexibly adapted based on external incentives. Thus, companies can economically benefit and at the same time contribute to reducing greenhouse gas emissions. However, there are currently some major obstacles that impede industrial companies from taking part in the energy markets. A broad specification analysis systematically dismantles the existing barriers. On this foundation, a new end-to-end ecosystem of an energy synchronization platform is introduced. It consists of a business-individual company-side platform, where suitable services for energy-oriented manufacturing are offered. In addition, one market-side platform is established as a mediating service broker, which connects the companies to, e.g., third party service providers, energy suppliers, aggregators, and energy markets. The ecosystems aim at preventing vendor lock-in and providing a flexible solution, relying on open standards and offering an integrated solution through an end-to-end energy flexibility data model. In this article, the resulting functionalities are discussed and the remaining deficits outlined.
The growing share of renewable power generation leads to increasingly fluctuating and generally rising electricity prices. This is a challenge for industrial companies. However, electricity expenses can be reduced by adapting the energy demand of production processes to the volatile prices on the markets. This approach depicts the new paradigm of energy flexibility to reduce electricity costs. At the same time, using electricity self-generation further offers possibilities for decreasing energy costs. In addition, energy flexibility can be gradually increased by on-site power storage, e.g., stationary batteries. As a consequence, both the electricity demand of the manufacturing system and the supply side, including battery storage, self-generation, and the energy market, need to be controlled in a holistic manner, thus resulting in a smart grid solution for industrial sites. This coordination represents a complex optimization problem, which additionally is highly stochastic due to unforeseen events like machine breakdowns, changing prices, or changing energy availability. This paper presents an approach to controlling a complex system of production resources, battery storage, electricity self-supply, and short-term market trading using multi-agent reinforcement learning (MARL). The results of a case study demonstrate that the developed system can outperform the rule-based reactive control strategy (RCS) frequently used. Although the metaheuristic benchmark based on simulated annealing performs better, MARL enables faster reactions because of the significantly lower computation costs for its own execution.
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